


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Any										=						(DPMSolverSinglestepScheduler,)
	_lowerCAmelCase     :		Any										=						(("""num_inference_steps""", 2_5),)


	def  A(    self				,     **lowercase__):
							__UpperCAmelCase			:							Union[str, Any]	    =	{
							    'num_train_timesteps': 1_0_0_0,
							    'beta_start': 0.0_0_0_1,
							    'beta_end': 0.0_2,
							    'beta_schedule': 'linear',
							    'solver_order': 2,
							    'prediction_type': 'epsilon',
							    'thresholding': False,
							    'sample_max_value': 1.0,
							    'algorithm_type': 'dpmsolver++',
							    'solver_type': 'midpoint',
							    'lambda_min_clipped': -float('''inf'''),
							    'variance_type': None,
							}

							config.update(**lowercase__)
							return config


	def  A(    self				,     lowercase__=0				,     **lowercase__):
							__UpperCAmelCase			:							Optional[Any]	    =	dict(self.forward_default_kwargs)
							__UpperCAmelCase			:							Any	    =	kwargs.pop('''num_inference_steps'''				,     lowercase__)
							__UpperCAmelCase			:							int	    =	self.dummy_sample
							__UpperCAmelCase			:							str	    =	0.1 * sample
							__UpperCAmelCase			:							List[Any]	    =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

							for scheduler_class in self.scheduler_classes:
													__UpperCAmelCase			:							int	    =	self.get_scheduler_config(**lowercase__)
													__UpperCAmelCase			:							Union[str, Any]	    =	scheduler_class(**lowercase__)
													scheduler.set_timesteps(lowercase__)
													# copy over dummy past residuals
													__UpperCAmelCase			:							Tuple	    =	dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowercase__)
																			__UpperCAmelCase			:							List[Any]	    =	scheduler_class.from_pretrained(lowercase__)
																			new_scheduler.set_timesteps(lowercase__)
																			# copy over dummy past residuals
																			__UpperCAmelCase			:							Tuple	    =	dummy_past_residuals[: new_scheduler.config.solver_order]

													__UpperCAmelCase			:							Dict	    =	sample, sample
													for t in range(lowercase__				,     time_step + scheduler.config.solver_order + 1):
																			__UpperCAmelCase			:							Optional[int]	    =	scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__).prev_sample
																			__UpperCAmelCase			:							Union[str, Any]	    =	new_scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__).prev_sample

																			assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"


	def  A(    self):
							pass


	def  A(    self				,     lowercase__=0				,     **lowercase__):
							__UpperCAmelCase			:							Tuple	    =	dict(self.forward_default_kwargs)
							__UpperCAmelCase			:							Optional[Any]	    =	kwargs.pop('''num_inference_steps'''				,     lowercase__)
							__UpperCAmelCase			:							Dict	    =	self.dummy_sample
							__UpperCAmelCase			:							List[Any]	    =	0.1 * sample
							__UpperCAmelCase			:							List[Any]	    =	[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

							for scheduler_class in self.scheduler_classes:
													__UpperCAmelCase			:							Optional[int]	    =	self.get_scheduler_config()
													__UpperCAmelCase			:							List[str]	    =	scheduler_class(**lowercase__)
													scheduler.set_timesteps(lowercase__)

													# copy over dummy past residuals (must be after setting timesteps)
													__UpperCAmelCase			:							List[Any]	    =	dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowercase__)
																			__UpperCAmelCase			:							Tuple	    =	scheduler_class.from_pretrained(lowercase__)
																			# copy over dummy past residuals
																			new_scheduler.set_timesteps(lowercase__)

																			# copy over dummy past residual (must be after setting timesteps)
																			__UpperCAmelCase			:							Optional[Any]	    =	dummy_past_residuals[: new_scheduler.config.solver_order]

													__UpperCAmelCase			:							str	    =	scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__).prev_sample
													__UpperCAmelCase			:							int	    =	new_scheduler.step(lowercase__				,     lowercase__				,     lowercase__				,     **lowercase__).prev_sample

													assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"


	def  A(    self				,     lowercase__=None				,     **lowercase__):
							if scheduler is None:
													__UpperCAmelCase			:							Any	    =	self.scheduler_classes[0]
													__UpperCAmelCase			:							Dict	    =	self.get_scheduler_config(**lowercase__)
													__UpperCAmelCase			:							List[Any]	    =	scheduler_class(**lowercase__)

							__UpperCAmelCase			:							List[str]	    =	self.scheduler_classes[0]
							__UpperCAmelCase			:							Optional[Any]	    =	self.get_scheduler_config(**lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	scheduler_class(**lowercase__)

							__UpperCAmelCase			:							Dict	    =	1_0
							__UpperCAmelCase			:							Optional[Any]	    =	self.dummy_model()
							__UpperCAmelCase			:							Optional[Any]	    =	self.dummy_sample_deter
							scheduler.set_timesteps(lowercase__)

							for i, t in enumerate(scheduler.timesteps):
													__UpperCAmelCase			:							List[str]	    =	model(lowercase__				,     lowercase__)
													__UpperCAmelCase			:							int	    =	scheduler.step(lowercase__				,     lowercase__				,     lowercase__).prev_sample

							return sample


	def  A(    self):
							__UpperCAmelCase			:							Union[str, Any]	    =	DPMSolverSinglestepScheduler(**self.get_scheduler_config())
							__UpperCAmelCase			:							str	    =	5_0
							__UpperCAmelCase			:							int	    =	self.dummy_model()
							__UpperCAmelCase			:							Union[str, Any]	    =	self.dummy_sample_deter
							scheduler.set_timesteps(lowercase__)

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]):
													__UpperCAmelCase			:							List[Any]	    =	model(lowercase__				,     lowercase__)
													__UpperCAmelCase			:							List[Any]	    =	scheduler.step(lowercase__				,     lowercase__				,     lowercase__).prev_sample

							__UpperCAmelCase			:							List[Any]	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_mean.item() - 0.2_5_7_4) < 1e-3


	def  A(    self):
							for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=lowercase__)


	def  A(    self):
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							__UpperCAmelCase			:							Union[str, Any]	    =	DPMSolverSinglestepScheduler(**self.get_scheduler_config())
							__UpperCAmelCase			:							str	    =	self.full_loop(scheduler=lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_mean.item() - 0.2_7_9_1) < 1e-3

							__UpperCAmelCase			:							Optional[int]	    =	DEISMultistepScheduler.from_config(scheduler.config)
							__UpperCAmelCase			:							int	    =	DPMSolverMultistepScheduler.from_config(scheduler.config)
							__UpperCAmelCase			:							str	    =	UniPCMultistepScheduler.from_config(scheduler.config)
							__UpperCAmelCase			:							List[Any]	    =	DPMSolverSinglestepScheduler.from_config(scheduler.config)

							__UpperCAmelCase			:							Union[str, Any]	    =	self.full_loop(scheduler=lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_mean.item() - 0.2_7_9_1) < 1e-3


	def  A(    self):
							self.check_over_configs(thresholding=lowercase__)
							for order in [1, 2, 3]:
													for solver_type in ["midpoint", "heun"]:
																			for threshold in [0.5, 1.0, 2.0]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    thresholding=lowercase__				,     prediction_type=lowercase__				,     sample_max_value=lowercase__				,     algorithm_type='''dpmsolver++'''				,     solver_order=lowercase__				,     solver_type=lowercase__				,     )


	def  A(    self):
							for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowercase__)


	def  A(    self):
							for algorithm_type in ["dpmsolver", "dpmsolver++"]:
													for solver_type in ["midpoint", "heun"]:
																			for order in [1, 2, 3]:
																									for prediction_type in ["epsilon", "sample"]:
																															self.check_over_configs(
																															    solver_order=lowercase__				,     solver_type=lowercase__				,     prediction_type=lowercase__				,     algorithm_type=lowercase__				,     )
																															__UpperCAmelCase			:							Optional[Any]	    =	self.full_loop(
																															    solver_order=lowercase__				,     solver_type=lowercase__				,     prediction_type=lowercase__				,     algorithm_type=lowercase__				,     )
																															assert not torch.isnan(lowercase__).any(), "Samples have nan numbers"


	def  A(    self):
							self.check_over_configs(lower_order_final=lowercase__)
							self.check_over_configs(lower_order_final=lowercase__)


	def  A(    self):
							self.check_over_configs(lambda_min_clipped=-float('''inf'''))
							self.check_over_configs(lambda_min_clipped=-5.1)


	def  A(    self):
							self.check_over_configs(variance_type=lowercase__)
							self.check_over_configs(variance_type='''learned_range''')


	def  A(    self):
							for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
													self.check_over_forward(num_inference_steps=lowercase__				,     time_step=0)


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	self.full_loop()
							__UpperCAmelCase			:							Any	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_mean.item() - 0.2_7_9_1) < 1e-3


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	self.full_loop(use_karras_sigmas=lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_mean.item() - 0.2_2_4_8) < 1e-3


	def  A(    self):
							__UpperCAmelCase			:							str	    =	self.full_loop(prediction_type='''v_prediction''')
							__UpperCAmelCase			:							List[str]	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_mean.item() - 0.1_4_5_3) < 1e-3


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	self.full_loop(prediction_type='''v_prediction'''				,     use_karras_sigmas=lowercase__)
							__UpperCAmelCase			:							Dict	    =	torch.mean(torch.abs(lowercase__))

							assert abs(result_mean.item() - 0.0_6_4_9) < 1e-3


	def  A(    self):
							__UpperCAmelCase			:							Tuple	    =	self.scheduler_classes[0]
							__UpperCAmelCase			:							Optional[Any]	    =	self.get_scheduler_config(thresholding=lowercase__				,     dynamic_thresholding_ratio=0)
							__UpperCAmelCase			:							str	    =	scheduler_class(**lowercase__)

							__UpperCAmelCase			:							Optional[int]	    =	1_0
							__UpperCAmelCase			:							Optional[int]	    =	self.dummy_model()
							__UpperCAmelCase			:							Optional[int]	    =	self.dummy_sample_deter.half()
							scheduler.set_timesteps(lowercase__)

							for i, t in enumerate(scheduler.timesteps):
													__UpperCAmelCase			:							Tuple	    =	model(lowercase__				,     lowercase__)
													__UpperCAmelCase			:							Any	    =	scheduler.step(lowercase__				,     lowercase__				,     lowercase__).prev_sample

							assert sample.dtype == torch.floataa









"""simple docstring"""


import heapq as hq
import math
from collections.abc import Iterator




class 						__UpperCamelCase     :
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	List[Any]   )	->       List[str]:
											lowerCAmelCase   :Dict						=			str(id_   )
											lowerCAmelCase   :str						=			None
											lowerCAmelCase   :Any						=			None
											lowerCAmelCase   :List[str]						=			[]
											lowerCAmelCase   :List[Any]						=			{}  # {vertex:distance}
					def __lt__(							self							:	Union[str, Any]     , UpperCAmelCase							:	Any   )	->       str:
											return self.key < other.key
					def __repr__(							self							:	Union[str, Any]   )	->       Dict:
											return self.id
					def        UpperCAmelCase__ (							self							:	Any     , UpperCAmelCase							:	Tuple   )	->       List[str]:
											self.neighbors.append(UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	str     , UpperCAmelCase							:	Optional[Any]     , UpperCAmelCase							:	List[Any]   )	->       Optional[int]:
											lowerCAmelCase   :str						=			weight


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						graph[a - 1].add_neighbor(graph[b - 1] )
						graph[b - 1].add_neighbor(graph[a - 1] )
						# add the edges:
						graph[a - 1].add_edge(graph[b - 1]     ,      a__ )
						graph[b - 1].add_edge(graph[a - 1]     ,      a__ )


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :List[str]						=			[]
						for u in graph:
												lowerCAmelCase   :Tuple						=			math.inf
												lowerCAmelCase   :Any						=			None
						lowerCAmelCase   :Dict						=			0
						lowerCAmelCase   :int						=			graph[:]
						while q:
												lowerCAmelCase   :str						=			min(a__ )
												q.remove(a__ )
												for v in u.neighbors:
																		if (v in q) and (u.edges[v.id] < v.key):
																								lowerCAmelCase   :List[str]						=			u
																								lowerCAmelCase   :List[Any]						=			u.edges[v.id]
						for i in range(1     ,      len(a__ ) ):
												a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
						return a


def        UpperCAmelCase      (					a__     ,      a__ ):





						'''simple docstring'''






						for u in graph:
												lowerCAmelCase   :List[str]						=			math.inf
												lowerCAmelCase   :str						=			None
						lowerCAmelCase   :Optional[int]						=			0

						lowerCAmelCase   :Dict						=			list(a__ )
						hq.heapify(a__ )

						while h:
												lowerCAmelCase   :Union[str, Any]						=			hq.heappop(a__ )
												for v in u.neighbors:
																		if (v in h) and (u.edges[v.id] < v.key):
																								lowerCAmelCase   :int						=			u
																								lowerCAmelCase   :List[Any]						=			u.edges[v.id]
																								hq.heapify(a__ )

						for i in range(1     ,      len(a__ ) ):
												yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)


def        UpperCAmelCase      (					):





						'''simple docstring'''








if __name__ == "__main__":
		import doctest

		doctest.testmod()